# A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs

arXiv: https://arxiv.org/abs/2401.10689 · HTML: https://mlsanity.com/paper/2401.10689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [Training and Inference with Integers in Deep Neural Networks](https://mlsanity.com/paper/1802.04680/) (2018)
- [GIDS: GAN based Intrusion Detection System for In-Vehicle Network](https://mlsanity.com/paper/1907.07377/) (2019)
- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [LUTNet: Rethinking Inference in FPGA Soft Logic](https://mlsanity.com/paper/1904.00938/) (2019)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [Using Data Analytics to Detect Anomalous States in Vehicles](https://mlsanity.com/paper/1512.08048/) (2015)
- [Viden: Attacker Identification on In-Vehicle Networks](https://mlsanity.com/paper/1708.08414/) (2017)
- [Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification](https://mlsanity.com/paper/1910.05765/) (2019)

## Built on by (most cited first, 1 of 1)

- [Behavioral Residualization for Unsupervised Intrusion Detection in Automotive CAN Networks](https://mlsanity.com/paper/2608.05548/) (2026)

## Abstract (arXiv)

Rising connectivity in vehicles is enabling new capabilities like connected autonomous driving and advanced driver assistance systems (ADAS) for improving the safety and reliability of next-generation vehicles. This increased access to in-vehicle functions compromises critical capabilities that use legacy invehicle networks like Controller Area Network (CAN), which has no inherent security or authentication mechanism. Intrusion detection and mitigation approaches, particularly using machine learning models, have shown promising results in detecting multiple attack vectors in CAN through their ability to generalise to new vectors. However, most deployments require dedicated computing units like GPUs to perform line-rate detection, consuming much higher power. In this paper, we present a lightweight multi-attack quantised machine learning model that is deployed using Xilinx's Deep Learning Processing Unit IP on a Zynq Ultrascale+ (XCZU3EG) FPGA, which is trained and validated using the public CAN Intrusion Detection dataset. The quantised model detects denial of service and fuzzing attacks with an accuracy of above 99 % and a false positive rate of 0.07%, which are comparable to the state-of-the-art techniques in the literature. The Intrusion Detection System (IDS) execution consumes just 2.0 W with software tasks running on the ECU and achieves a 25 % reduction in per-message processing latency over the state-of-the-art implementations. This deployment allows the ECU function to coexist with the IDS with minimal changes to the tasks, making it ideal for real-time IDS in in-vehicle systems.